2024/07/10 by Yue Wang, Wang, Yue, Haoran Shi +1
Biochemistry, Genetics and Molecular Biology · #FOS: Computer and information sciences #Gene expression and cancer classification #Methodology (stat.ME)
paper · pdf · doi:10.48550/arxiv.2407.07809
openalex publication_date 2024/07/10 · openalex created_date 2024/07/13 · openalex updated_date 2026/07/28
This paper tackles the challenge of estimating correlations between higher-level biological variables (e.g., proteins and gene pathways) when only lower-level measurements are directly observed (e.g., peptides and individual genes). Existing methods typically aggregate lower-level data into higher-level variables and then estimate correlations based on the aggregated data. However, different data aggregation methods can yield varying correlation estimates as they target different higher-level quantities. Our solution is a latent factor model that directly estimates these higher-level correlations from lower-level data without the need for data aggregation. We further introduce a shrinkage estimator to ensure the positive definiteness and improve the accuracy of the estimated correlation matrix. Furthermore, we establish the asymptotic normality of our estimator, enabling efficient computation of p-values for the identification of significant correlations. The effectiveness of our approach is demonstrated through comprehensive simulations and the analysis of proteomics and gene expression datasets. We develop the R package highcor for implementing our method.